In [1]} the authors use a normalizing flow-based causal model similar to [2]} in order to harmonize heterogeneous medical data. Applied to T1 brain MRI for the classification of Alzheimer's disease, the method abides by the abduction-action-prediction paradigm to infer counterfactuals which are then used to harmonize the medical data.
[3]} circumvent the identifiability condition that all confounders have to be known and measured by leveraging the dependencies between causes in order to determine substitute confounders; they apply their method in brain neuroimaging for Alzheimer's disease detection.
On a similar note,
[4]} issue an alternative to expectation maximization(EM) for dynamic causal modeling in f-MRI brain scans. Their approach is based upon the multiple-shooting method to estimate the parameters of ordinary differential equations (ODEs) under noisy observations required for brain causal modeling. The authors suggest an augmentation of the aforementioned method called multiple-shooting adjoint method by using the adjoint method to calculate the loss and gradients of their model.
